Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of assessing the health status of a patient comprising: evaluating the presence of volatile organic compounds in a breath or gas sample of the patient with a plurality of graphene sensors to generate volatile organic compound data, wherein the plurality of graphene sensors include sensors that are specific for different volatile organic compounds; collecting data regarding the patient's sympathetic nervous activity, the data comprising at least one of heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); combining the volatile organic compound data with the collected data regarding the patient's sympathetic nervous activity to form a combined data set; and matching the combined data set against one or more previously determined data patterns using a pattern matching algorithm to determine the data pattern that is the best match, wherein the specific previously determined data pattern that is the best match indicates the health status of the patient.
This invention relates to medical diagnostics and specifically addresses the problem of non-invasively assessing a patient's health status. The method involves analyzing a breath or gas sample from a patient using multiple graphene sensors. These sensors are designed to detect and quantify specific volatile organic compounds (VOCs) present in the sample, thereby generating VOC data. Concurrently, data related to the patient's sympathetic nervous system activity is collected. This sympathetic nervous activity data can include measurements such as heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS). The VOC data and the sympathetic nervous activity data are then combined to create a comprehensive data set. This combined data set is subsequently compared against a library of pre-established data patterns using a pattern matching algorithm. The algorithm identifies the previously determined data pattern that most closely aligns with the patient's combined data set. The specific matched data pattern serves to indicate the patient's health status.
2. The method of claim 1 , wherein the one or more previously determined data patterns are created using a machine learning process.
A system and method for analyzing data patterns to improve decision-making processes. The technology addresses the challenge of efficiently identifying relevant data patterns in large datasets to support automated or semi-automated decision-making. The method involves collecting data from one or more sources, processing the data to extract features, and applying a machine learning process to identify and create one or more data patterns. These patterns are then used to generate insights or predictions, which are applied to a decision-making process. The machine learning process may involve supervised or unsupervised learning techniques, such as clustering, classification, or regression, to detect meaningful relationships or trends in the data. The system may also include a feedback mechanism to refine the data patterns over time based on new data or user input. The method ensures that the data patterns are dynamically updated to maintain accuracy and relevance, improving the reliability of the decision-making process. The technology is applicable in various fields, including finance, healthcare, and manufacturing, where data-driven insights are critical for optimizing outcomes.
3. The method of claim 1 , further comprising collecting data regarding the patient's functional status, the data comprising gait data; and adding the data regarding the patient's functional status to the combined data set.
This invention relates to a method for enhancing patient monitoring and assessment by integrating functional status data, particularly gait data, into a combined data set for analysis. The method addresses the challenge of obtaining a comprehensive view of a patient's health by incorporating objective measurements of physical function alongside other clinical data. Gait data, which includes metrics such as walking speed, stride length, and balance, provides critical insights into mobility, neurological function, and overall physical health. By collecting and adding this data to a combined data set, the method enables more accurate and holistic patient assessments. The combined data set may include additional patient information such as vital signs, laboratory results, or imaging data, allowing for a multifaceted analysis of the patient's condition. This approach supports early detection of functional decline, personalized treatment planning, and continuous monitoring of patient progress. The integration of gait data with other clinical data improves diagnostic accuracy and treatment effectiveness, particularly for conditions affecting mobility, such as Parkinson's disease, stroke, or musculoskeletal disorders. The method leverages wearable or sensor-based technologies to capture gait metrics, ensuring non-invasive and scalable data collection. By analyzing the combined data set, healthcare providers can make data-driven decisions to optimize patient care and outcomes.
4. The method of claim 1 , further comprising collecting data regarding the patient's demographic features; and adding the data regarding the patient's demographic features to the combined data set.
This invention relates to a method for enhancing medical data analysis by incorporating patient demographic features into a combined data set. The method addresses the challenge of improving the accuracy and relevance of medical insights by integrating demographic information, such as age, gender, ethnicity, or socioeconomic status, with clinical data. This helps identify patterns and trends that may be influenced by demographic factors, leading to more personalized and effective healthcare solutions. The method involves collecting demographic data from patients, which may include age, gender, ethnicity, income level, or other relevant factors. This data is then added to a combined data set that already includes clinical information, such as medical history, test results, or treatment outcomes. By analyzing the combined data set, healthcare providers can better understand how demographic variables impact health conditions, treatment responses, and overall patient outcomes. This integration allows for more targeted interventions and improved decision-making in patient care. The method ensures that demographic data is properly collected, validated, and integrated into the existing data set to maintain data integrity and reliability. This approach enhances the depth and breadth of medical data analysis, enabling more comprehensive insights and better-informed healthcare strategies.
5. The method of claim 1 , wherein collecting the data regarding the patient's sympathetic nervous activity is performed in a non-clinical setting and evaluating the presence of the volatile organic compounds is performed in a clinical setting.
This invention relates to a method for monitoring and evaluating a patient's sympathetic nervous system activity by analyzing volatile organic compounds (VOCs) in exhaled breath. The method addresses the challenge of assessing autonomic nervous system dysfunction, which is often difficult to detect in non-clinical environments where traditional diagnostic tools are unavailable. The method involves collecting data on the patient's sympathetic nervous activity in a non-clinical setting, such as the patient's home or workplace, using portable or wearable devices. This data collection may include measuring physiological parameters like heart rate variability, skin conductance, or other biomarkers indicative of sympathetic nervous system activity. The collected data is then transmitted to a clinical setting, where it is analyzed alongside the evaluation of VOCs present in the patient's exhaled breath. The VOC analysis is performed in a clinical environment using specialized equipment capable of detecting and quantifying these compounds, which are known to correlate with sympathetic nervous system activity. By separating the data collection and analysis processes between non-clinical and clinical settings, the method enables continuous, real-world monitoring of sympathetic nervous system function while leveraging the precision of clinical-grade diagnostic tools. This approach improves early detection and management of conditions related to autonomic dysfunction, such as cardiovascular diseases, stress-related disorders, and metabolic syndromes.
6. The method of claim 1 , wherein collecting the data regarding the patient's sympathetic nervous activity is performed with a wearable device.
A wearable device is used to collect data regarding a patient's sympathetic nervous activity, which is part of a broader method for monitoring and analyzing autonomic nervous system function. The sympathetic nervous system regulates involuntary physiological responses such as heart rate, blood pressure, and stress reactions. The wearable device continuously or periodically measures physiological signals, such as heart rate variability, skin conductance, or muscle activity, to assess sympathetic activity. This data is then processed to detect patterns or anomalies indicative of stress, fatigue, or other physiological states. The wearable device may be a wristband, chest strap, or other form-fitting sensor that ensures consistent contact with the patient's body for accurate signal acquisition. The collected data is transmitted wirelessly to a processing unit, which analyzes the signals to derive insights into the patient's autonomic function. This method is particularly useful in medical monitoring, stress management, and chronic condition tracking, where continuous, non-invasive assessment of sympathetic activity is beneficial. The wearable device may also include additional sensors to capture complementary data, such as temperature or movement, to enhance the accuracy of the analysis. The system may further provide real-time feedback or alerts to the patient or healthcare provider based on the detected sympathetic activity.
7. The method of claim 1 , wherein collecting the data regarding the patient's sympathetic nervous activity is performed over a time period of at least about 1 day.
This invention relates to monitoring a patient's sympathetic nervous system activity over an extended period to assess physiological conditions. The method involves collecting data on sympathetic nervous activity, such as heart rate variability, blood pressure fluctuations, or skin conductance, over a minimum of one day. This extended monitoring period allows for more accurate detection of patterns or abnormalities in sympathetic activity that may be indicative of stress, cardiovascular disorders, or other medical conditions. The collected data is then analyzed to identify deviations from normal sympathetic nervous system function, which can inform clinical decisions or treatment adjustments. The method may be used in conjunction with other diagnostic tools to provide a comprehensive assessment of a patient's autonomic nervous system health. By capturing data over an extended duration, the approach reduces the risk of misdiagnosis due to short-term fluctuations and provides a more reliable basis for medical evaluation. The technique is particularly useful in conditions where sympathetic nervous system dysregulation plays a significant role, such as hypertension, post-traumatic stress disorder, or autonomic dysfunction. The system may incorporate wearable or implantable sensors to continuously track physiological parameters, ensuring minimal disruption to the patient's daily activities. The analysis may involve machine learning algorithms or statistical models to detect subtle changes in sympathetic activity that could indicate underlying health issues. This method enhances the accuracy and reliability of autonomic nervous system assessments, supporting better patient care and early intervention.
8. The method of claim 1 , wherein collecting the data regarding the patient's sympathetic nervous activity is performed with an implanted device.
This invention relates to monitoring and analyzing a patient's sympathetic nervous activity using an implanted medical device. The sympathetic nervous system plays a key role in regulating physiological responses, and abnormal activity can indicate or contribute to various medical conditions. The invention addresses the challenge of accurately and continuously measuring sympathetic nervous activity in a clinical setting, which is difficult with traditional surface-based or intermittent monitoring techniques. The method involves collecting data regarding the sympathetic nervous activity through an implanted device, which provides direct and continuous measurements. The implanted device may include sensors or electrodes designed to detect physiological signals indicative of sympathetic activity, such as heart rate variability, blood pressure fluctuations, or neural signals. The collected data is then processed to assess the patient's sympathetic nervous system function, enabling early detection of abnormalities or conditions like hypertension, arrhythmias, or autonomic dysfunction. The implanted device may also communicate with an external system for further analysis, allowing healthcare providers to monitor the patient remotely. By using an implanted device, the invention ensures more reliable and consistent data collection compared to external monitoring methods, improving diagnostic accuracy and treatment planning. The system may also incorporate machine learning or pattern recognition to identify trends or anomalies in the collected data, enhancing its clinical utility. This approach supports personalized medicine by providing continuous, real-time insights into a patient's autonomic nervous system function.
9. The method of claim 1 , wherein the volatile organic compound data from the breath or gas sample of the patient is downloaded from an external breath sensing system onto at least one of a wearable device and an implantable device.
This invention relates to medical diagnostics, specifically the detection and analysis of volatile organic compounds (VOCs) in breath or gas samples to assess patient health. The technology addresses the challenge of non-invasive, real-time monitoring of biomarkers for early disease detection or health tracking. The method involves collecting VOC data from a patient's breath or gas sample using an external breath sensing system. This data is then transferred to either a wearable device or an implantable device for further processing and analysis. The wearable or implantable device may include sensors, processors, or communication modules to interpret the VOC data and provide health insights. The system enables continuous or periodic monitoring without requiring frequent manual sample collection, improving convenience and patient compliance. The external breath sensing system may use techniques such as gas chromatography, mass spectrometry, or chemical sensors to detect VOCs, while the wearable or implantable device may integrate this data with other physiological metrics for comprehensive health assessment. This approach supports remote patient monitoring and personalized healthcare by leveraging portable and minimally invasive technologies.
10. The method of claim 1 , wherein the collected data regarding the patient's sympathetic nervous activity is uploaded from a wearable device to a clinical diagnostic device.
A system and method for monitoring and analyzing a patient's sympathetic nervous activity involves collecting physiological data from a wearable device worn by the patient. The wearable device measures parameters such as heart rate variability, skin conductance, or other indicators of sympathetic nervous system activity. The collected data is then transmitted wirelessly or via a wired connection to a clinical diagnostic device, which processes the information to assess the patient's autonomic function. The clinical diagnostic device may include software algorithms that analyze the data to detect patterns, anomalies, or trends indicative of sympathetic nervous system dysfunction or stress-related conditions. The system may also provide real-time feedback to the patient or healthcare provider, enabling early intervention or treatment adjustments. The wearable device may be configured to continuously or intermittently collect data, depending on the patient's condition and monitoring requirements. The clinical diagnostic device may further integrate with electronic health records or other medical systems to provide a comprehensive view of the patient's autonomic health. This approach improves the accuracy and convenience of sympathetic nervous system monitoring, enabling better diagnosis and management of related disorders.
11. The method of claim 1 , wherein one or more of the plurality of graphene sensors are chosen as controls on the collected data regarding the patient's sympathetic nervous activity.
This invention relates to a medical monitoring system that uses graphene sensors to measure and analyze a patient's sympathetic nervous system activity. The system addresses the challenge of accurately detecting and interpreting sympathetic nervous system responses, which are critical for diagnosing and managing conditions like hypertension, stress-related disorders, and autonomic dysfunction. Traditional monitoring methods often lack precision or require invasive procedures. The system employs multiple graphene sensors strategically placed on a patient's body to capture electrical signals associated with sympathetic nervous activity. These sensors are highly sensitive and capable of detecting subtle physiological changes. The invention includes a control mechanism that selects one or more of the graphene sensors to serve as reference points for calibrating and validating the collected data. This ensures accuracy by comparing the readings from active sensors against the control sensors, which may be positioned in areas with minimal sympathetic activity or used to account for environmental interference. The control sensors help distinguish true physiological signals from noise or artifacts, improving the reliability of the monitoring system. The data is processed to provide real-time or retrospective insights into the patient's autonomic function, aiding in diagnosis and treatment planning.
12. The method of claim 11 , wherein the controls correlate with sympathetic nervous activity.
The invention relates to a system for monitoring and regulating physiological states, particularly focusing on the sympathetic nervous system. The sympathetic nervous system plays a key role in stress responses, and abnormal activity can lead to health issues. The invention provides a method to detect and analyze sympathetic nervous activity in real-time, enabling targeted interventions to maintain physiological balance. The method involves using sensors to measure physiological signals, such as heart rate variability, skin conductance, or muscle activity, which are indicative of sympathetic nervous system activity. These signals are processed to extract features that correlate with sympathetic activation. The system then generates control signals that adjust external stimuli, such as biofeedback cues or environmental conditions, to modulate sympathetic activity. For example, if elevated sympathetic activity is detected, the system may trigger relaxation exercises or adjust lighting to reduce stress. The invention also includes feedback mechanisms to refine the correlation between the detected signals and sympathetic activity over time, improving accuracy. By continuously monitoring and adjusting interventions, the system helps regulate stress responses and promote well-being. This approach is applicable in medical, wellness, and performance optimization contexts, such as stress management, rehabilitation, and athletic training. The method ensures precise and adaptive control of sympathetic nervous activity, enhancing overall physiological regulation.
13. The method of claim 12 , further comprising generating a notification if the measured values of the controls do not match the measured values of sympathetic nervous activity.
A system and method for monitoring and analyzing physiological responses to detect discrepancies between user-controlled inputs and autonomic nervous system activity. The invention addresses the need to identify inconsistencies between voluntary actions and involuntary physiological reactions, which may indicate stress, deception, or medical conditions. The method involves measuring values of user-controlled inputs, such as muscle activity or movement, and simultaneously measuring sympathetic nervous system activity, such as heart rate variability or skin conductance. The system compares these measured values to determine if they align as expected. If the measured values of the controls do not match the measured values of sympathetic nervous activity, the system generates a notification to alert the user or an operator. This discrepancy detection can be used in applications such as stress monitoring, lie detection, or medical diagnostics. The method may also include preprocessing the measured signals to remove noise and artifacts before comparison. The system may further adjust sensitivity thresholds based on historical data or user-specific baselines to improve accuracy. The notification can be visual, auditory, or haptic, depending on the application. This approach provides a non-invasive way to assess physiological coherence and detect potential anomalies in real-time.
14. The method of claim 1 , wherein the collected data regarding the patient's sympathetic nervous activity reflects a baseline level of sympathetic nervous activity and changes over the baseline level of sympathetic nervous activity.
This invention relates to monitoring and analyzing a patient's sympathetic nervous system activity to assess physiological states. The sympathetic nervous system plays a key role in regulating stress responses, cardiovascular function, and other autonomic processes. The invention addresses the challenge of accurately measuring and interpreting sympathetic activity in real-time to detect deviations from normal baseline levels, which can indicate stress, pain, or other physiological disturbances. The method involves collecting data that reflects both the baseline level of sympathetic nervous activity and subsequent changes from this baseline. This data may be gathered using various physiological sensors, such as electrodermal activity sensors, heart rate variability monitors, or other biometric devices. The collected data is processed to distinguish between normal baseline activity and significant deviations, which may signal an abnormal physiological state. The system may also correlate these changes with external factors, such as environmental stimuli or medical interventions, to provide context for the observed variations. By continuously tracking sympathetic activity and its fluctuations, the invention enables early detection of stress-related conditions, pain responses, or other autonomic dysfunctions. This can be particularly useful in clinical settings, such as during surgery, anesthesia, or intensive care, where monitoring sympathetic activity can help guide treatment decisions. The method may also be applied in wearable health devices for long-term monitoring of stress levels in everyday life. The invention improves upon existing techniques by providing a more comprehensive and dynamic assessment of sympathetic nervous system function.
15. The method of claim 1 , wherein the plurality of graphene sensors can detect the presence of at least 10 different volatile organic compounds.
This invention relates to a method for detecting volatile organic compounds (VOCs) using graphene-based sensors. The problem addressed is the need for highly sensitive, selective, and efficient detection of multiple VOCs, which are often indicators of environmental pollution, industrial leaks, or health conditions. Traditional sensors often lack the sensitivity or specificity required for reliable detection of diverse VOCs. The method employs a plurality of graphene sensors, each configured to detect the presence of at least 10 different VOCs. Graphene's high surface area and electrical conductivity make it highly sensitive to molecular interactions, allowing for precise detection. The sensors may be functionalized or modified to enhance selectivity for specific VOCs, ensuring accurate identification. The system may include signal processing components to analyze and differentiate between detected VOCs, providing real-time data. This approach enables rapid, multi-analyte detection in applications such as environmental monitoring, industrial safety, and medical diagnostics. The graphene sensors can be integrated into compact, portable devices for field use or deployed in fixed monitoring stations. The method improves upon existing technologies by offering higher sensitivity, broader detection capabilities, and potential cost-effectiveness due to graphene's scalable production.
16. A diagnostic health system comprising: a communications circuit; a memory circuit; and a processor in electronic communication with the communication circuit and the memory circuit, the processor is configured to combine volatile organic compound data with collected data regarding a patient's sympathetic nervous activity to form a combined data set, the collected data comprising at least one of heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); and match the combined data set against one or more previously determined data patterns using a pattern matching algorithm to determine a pattern that is the best match, wherein the specific previously determined pattern that is the best match indicates the health status of the patient; and report the health status of the patient based on the best pattern match.
A diagnostic health system analyzes a patient's health status by integrating volatile organic compound (VOC) data with physiological measurements of sympathetic nervous system activity. The system collects data on heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS) to assess autonomic nervous system function. These measurements are combined with VOC data to form a comprehensive dataset. The system then compares this dataset against pre-established data patterns using a pattern-matching algorithm to identify the closest match. The best-matching pattern indicates the patient's health status, which is then reported. This approach leverages both biochemical and physiological signals to provide a more accurate and holistic assessment of health conditions. The system is designed to enhance diagnostic precision by correlating multiple physiological and biochemical markers, improving early detection and monitoring of health issues.
17. The diagnostic health system of claim 16 , wherein the diagnostic health system is a wearable device and the volatile organic compound data is downloaded onto the wearable device from another device.
The invention relates to a diagnostic health system designed to monitor and analyze volatile organic compounds (VOCs) in a user's breath or bodily fluids to detect health conditions. The system includes sensors that capture VOC data, a processing unit that analyzes the data to identify biomarkers associated with diseases or physiological states, and an output interface that provides health insights to the user. The system may also include a database of reference VOC profiles for comparison. In one embodiment, the diagnostic health system is a wearable device, and the VOC data is transferred from another device, such as a smartphone or a dedicated sensor, to the wearable device for analysis. This allows the wearable to provide real-time or on-demand health monitoring without requiring integrated VOC sensors. The system may further include communication modules to transmit data to healthcare providers or cloud-based platforms for further analysis. The invention aims to enable early disease detection, personalized health monitoring, and proactive health management by leveraging VOC analysis in a portable, user-friendly format.
18. The diagnostic health system of claim 16 , wherein the diagnostic health system is disposed in a clinical environment and collected data regarding a patient's sympathetic nervous activity is uploaded to the diagnostic health system from a wearable device.
The system relates to diagnostic health monitoring in clinical environments, specifically focusing on tracking a patient's sympathetic nervous system activity. The system collects physiological data from wearable devices worn by patients, which are then transmitted to the system for analysis. The wearable devices continuously monitor sympathetic nervous activity, such as heart rate variability, skin conductance, or other relevant biomarkers, providing real-time or periodic data to the diagnostic health system. The system processes this data to assess the patient's autonomic nervous system function, detect stress responses, or identify potential health conditions linked to sympathetic overactivity. The clinical environment ensures that the system operates in a controlled setting, such as a hospital or medical facility, where healthcare professionals can access the collected data for diagnostic or treatment purposes. The integration of wearable devices allows for non-invasive, continuous monitoring, reducing the need for frequent manual measurements while improving the accuracy and timeliness of health assessments. This approach enhances patient care by enabling early detection of abnormalities and facilitating personalized treatment plans based on sympathetic nervous system activity patterns.
19. A diagnostic health system comprising: a patient-specific device selected from the group consisting of a wearable device and an implanted device; and an external breath sensing system; and a processor receiving data from the patient-specific device and the external breath sensing system; wherein the patient-specific device collects data regarding a patient's sympathetic nervous activity, the data comprising at least one of heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS); wherein the external breath sensing system collects data regarding the presence of volatile organic compounds in a breath or gas sample of the patient; and wherein the processor is configured to combine the volatile organic compound data with the patient's sympathetic nervous activity data to form a combined data set; and match the combined data set against one or more previously determined data patterns using a pattern matching algorithm to determine a pattern that is the best match, wherein the specific previously determined pattern that is the best match indicates the health status of the patient; and report the health status of the patient based on the best pattern match.
A diagnostic health system integrates patient-specific devices, such as wearable or implanted sensors, with an external breath sensing system to monitor health status. The patient-specific device measures sympathetic nervous activity, including heart rate variability (HRV), electrodermal activity (EDA), respiratory sinus arrhythmia (RSA), and baroreceptor sensitivity (BRS). The external breath sensing system detects volatile organic compounds (VOCs) in the patient's breath or gas sample. A processor combines the sympathetic nervous activity data with the VOC data to form a unified dataset. This combined data is compared against pre-established data patterns using a pattern matching algorithm to identify the closest match. The matched pattern indicates the patient's health status, which is then reported. This system enables comprehensive health monitoring by correlating physiological and biochemical markers for early detection of health conditions.
Unknown
September 8, 2020
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.